1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
2/7/2023 Parafina 19418 Tami NA
4/7/2023 Comida 12000 Andrés nueces y almendras 500 gr
6/7/2023 Gas 68950 Andrés lipigas
8/7/2023 Agua 12706 Andrés NA
8/7/2023 Comida 57693 Tami Supermercado
9/7/2023 correo 8000 Andrés correos de chile raul miranda
9/7/2023 mouse 51980 Andrés NA
9/7/2023 lamina 13800 Andrés NA
12/7/2023 Comida 26780 Andrés NA
12/7/2023 Netflix 11880 Tami Netflix junio y julio 2023
17/7/2023 Comida 86974 Tami Supermercado
19/7/2023 VTR 21990 Andrés NA
25/7/2023 Comida 75171 Tami Supermercado
24/7/2023 Enceres 27065 Andrés secador platos
30/7/2023 Comida 19630 Andrés choritos, costa rama y weas
30/7/2023 Parafina 19920 Tami NA
30/7/2023 Electricidad 49345 Andrés PAC ENEL 01686518
31/7/2023 Comida 78380 Tami Supermercado
3/8/2023 Comida 19000 Andrés NA
3/8/2023 Diosi 15980 Andrés NA
6/8/2023 Gas 16650 Andrés NA
6/8/2023 Gas 23666 Tami Parafina
8/8/2023 Comida 78577 Tami Supermercado
9/8/2023 Agua 11520 Andrés NA
15/8/2023 Comida 51910 Tami Supermercado
16/8/2023 Bencina + peajes Maite 49000 Tami NA
16/8/2023 Comida 13500 Tami Maitemarket
20/8/2023 VTR 21990 Andrés NA
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      sjPlot::theme_sjplot2() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
  tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
    theme_bw()+ labs(x="Weeks")

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 6.8347e+08   2    7.1515  9e-04 ***
## lag_depvar    8.4619e+10   1 1770.8266 <2e-16 ***
## Residuals     2.9053e+10 608                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff       lwr      upr     p adj
## 1-0  7228.838  1129.181 13328.49 0.0152517
## 2-0 28543.274 22986.358 34100.19 0.0000000
## 2-1 21314.436 18021.197 24607.67 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
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## 231  80355.00             2   86724.86
## 232  74875.14             2   80355.00
## 233  81347.00             2   74875.14
## 234  66062.43             2   81347.00
## 235  56946.43             2   66062.43
## 236  47732.14             2   56946.43
## 237  38129.71             2   47732.14
## 238  42928.29             2   38129.71
## 239  45392.57             2   42928.29
## 240  37895.43             2   45392.57
## 241  30660.29             2   37895.43
## 242  42430.86             2   30660.29
## 243  35845.14             2   42430.86
## 244  40350.43             2   35845.14
## 245  31494.71             2   40350.43
## 246  30013.29             2   31494.71
## 247  34197.57             2   30013.29
## 248  37430.14             2   34197.57
## 249  26932.43             2   37430.14
## 250  33729.86             2   26932.43
## 251  38081.43             2   33729.86
## 252  44028.00             2   38081.43
## 253  47139.71             2   44028.00
## 254  46558.86             2   47139.71
## 255  58350.57             2   46558.86
## 256  78380.00             2   58350.57
## 257  78168.29             2   78380.00
## 258  70510.86             2   78168.29
## 259  72207.14             2   70510.86
## 260  67881.00             2   72207.14
## 261  69536.43             2   67881.00
## 262  62390.71             2   69536.43
## 263  50113.14             2   62390.71
## 264  45565.57             2   50113.14
## 265  45805.29             2   45565.57
## 266  41348.57             2   45805.29
## 267  51426.86             2   41348.57
## 268  47160.57             2   51426.86
## 269  51907.43             2   47160.57
## 270  49751.43             2   51907.43
## 271  54407.43             2   49751.43
## 272  54746.29             2   54407.43
## 273  61634.57             2   54746.29
## 274  58926.43             2   61634.57
## 275  69999.29             2   58926.43
## 276  63044.86             2   69999.29
## 277  63285.29             2   63044.86
## 278  61395.43             2   63285.29
## 279  67969.43             2   61395.43
## 280  60792.57             2   67969.43
## 281  56859.14             2   60792.57
## 282  44899.43             2   56859.14
## 283  43064.14             2   44899.43
## 284  62790.29             2   43064.14
## 285  69120.71             2   62790.29
## 286  69589.43             2   69120.71
## 287  66633.29             2   69589.43
## 288  65588.57             2   66633.29
## 289  70168.57             2   65588.57
## 290  74644.71             2   70168.57
## 291  52891.00             2   74644.71
## 292  41560.57             2   52891.00
## 293  34704.86             2   41560.57
## 294  46520.00             2   34704.86
## 295  50231.00             2   46520.00
## 296  49216.71             2   50231.00
## 297  76914.86             2   49216.71
## 298  83720.71             2   76914.86
## 299  84485.00             2   83720.71
## 300  89765.00             2   84485.00
## 301  87702.86             2   89765.00
## 302  82013.86             2   87702.86
## 303  85982.43             2   82013.86
## 304  57248.43             2   85982.43
## 305  52968.43             2   57248.43
## 306  52601.86             2   52968.43
## 307  45493.29             2   52601.86
## 308  42298.86             2   45493.29
## 309  46423.71             2   42298.86
## 310  37898.00             2   46423.71
## 311  36435.14             2   37898.00
## 312  30209.57             2   36435.14
## 313  34541.86             2   30209.57
## 314  33604.71             2   34541.86
## 315  37990.71             2   33604.71
## 316  35683.43             2   37990.71
## 317  65201.86             2   35683.43
## 318  62730.57             2   65201.86
## 319  64589.14             2   62730.57
## 320  73744.86             2   64589.14
## 321  76477.71             2   73744.86
## 322 105647.43             2   76477.71
## 323 103790.29             2  105647.43
## 324  76122.29             2  103790.29
## 325  74746.14             2   76122.29
## 326  72865.71             2   74746.14
## 327  63652.57             2   72865.71
## 328  60358.29             2   63652.57
## 329  25957.14             2   60358.29
## 330  30178.43             2   25957.14
## 331  30681.57             2   30178.43
## 332  33337.29             2   30681.57
## 333  32582.71             2   33337.29
## 334  39184.43             2   32582.71
## 335  40415.71             2   39184.43
## 336  34975.43             2   40415.71
## 337  34076.14             2   34975.43
## 338  34221.14             2   34076.14
## 339  28862.57             2   34221.14
## 340  35729.86             2   28862.57
## 341  36489.29             2   35729.86
## 342  36785.14             2   36489.29
## 343  37787.71             2   36785.14
## 344  39832.14             2   37787.71
## 345  41917.86             2   39832.14
## 346  41633.57             2   41917.86
## 347  33557.00             2   41633.57
## 348  22759.57             2   33557.00
## 349  28877.86             2   22759.57
## 350  27574.00             2   28877.86
## 351  27104.71             2   27574.00
## 352  24376.14             2   27104.71
## 353  29732.29             2   24376.14
## 354  34030.00             2   29732.29
## 355  39139.71             2   34030.00
## 356  37066.57             2   39139.71
## 357  38509.29             2   37066.57
## 358  40957.29             2   38509.29
## 359  49423.00             2   40957.29
## 360  50053.29             2   49423.00
## 361  50284.14             2   50053.29
## 362  53103.86             2   50284.14
## 363  50223.00             2   53103.86
## 364  49587.14             2   50223.00
## 365  41167.71             2   49587.14
## 366  37958.71             2   41167.71
## 367  33582.29             2   37958.71
## 368  31039.43             2   33582.29
## 369  26526.57             2   31039.43
## 370  34869.43             2   26526.57
## 371  37487.43             2   34869.43
## 372  46514.43             2   37487.43
## 373  39613.43             2   46514.43
## 374  38980.57             2   39613.43
## 375  37306.14             2   38980.57
## 376  36771.29             2   37306.14
## 377  26317.00             2   36771.29
## 378  31580.71             2   26317.00
## 379  23626.57             2   31580.71
## 380  33035.71             2   23626.57
## 381  44864.57             2   33035.71
## 382  48946.14             2   44864.57
## 383  46969.57             2   48946.14
## 384  49249.57             2   46969.57
## 385  56370.14             2   49249.57
## 386  67228.71             2   56370.14
## 387  59457.29             2   67228.71
## 388  53124.71             2   59457.29
## 389  52814.14             2   53124.71
## 390  61262.00             2   52814.14
## 391  61861.14             2   61262.00
## 392  71784.71             2   61861.14
## 393  59313.29             2   71784.71
## 394  61107.00             2   59313.29
## 395  60603.43             2   61107.00
## 396  60012.57             2   60603.43
## 397  58280.43             2   60012.57
## 398  56862.71             2   58280.43
## 399  41704.43             2   56862.71
## 400  51533.00             2   41704.43
## 401  50388.71             2   51533.00
## 402  49205.29             2   50388.71
## 403  56533.29             2   49205.29
## 404  47996.14             2   56533.29
## 405  47207.57             2   47996.14
## 406  45292.00             2   47207.57
## 407  40343.43             2   45292.00
## 408  39004.86             2   40343.43
## 409  36788.43             2   39004.86
## 410  30027.57             2   36788.43
## 411  39040.14             2   30027.57
## 412  42390.14             2   39040.14
## 413  36291.14             2   42390.14
## 414  30668.29             2   36291.14
## 415  47693.00             2   30668.29
## 416  52094.43             2   47693.00
## 417  56592.57             2   52094.43
## 418  47971.43             2   56592.57
## 419  43762.43             2   47971.43
## 420  42246.71             2   43762.43
## 421  46352.43             2   42246.71
## 422  33094.86             2   46352.43
## 423  32784.86             2   33094.86
## 424  26212.43             2   32784.86
## 425  32611.57             2   26212.43
## 426  42144.86             2   32611.57
## 427  50034.86             2   42144.86
## 428  46332.00             2   50034.86
## 429  42976.29             2   46332.00
## 430  39456.29             2   42976.29
## 431  39328.29             2   39456.29
## 432  35296.14             2   39328.29
## 433  30875.43             2   35296.14
## 434  27709.00             2   30875.43
## 435  29513.29             2   27709.00
## 436  31630.43             2   29513.29
## 437  29346.14             2   31630.43
## 438  34916.86             2   29346.14
## 439  42020.86             2   34916.86
## 440  38303.00             2   42020.86
## 441  37966.43             2   38303.00
## 442  41408.14             2   37966.43
## 443  38988.14             2   41408.14
## 444  43555.29             2   38988.14
## 445  38114.00             2   43555.29
## 446  27847.86             2   38114.00
## 447  26517.00             2   27847.86
## 448  39518.29             2   26517.00
## 449  39153.71             2   39518.29
## 450  45623.14             2   39153.71
## 451  40627.43             2   45623.14
## 452  41027.71             2   40627.43
## 453  42882.86             2   41027.71
## 454  47139.43             2   42882.86
## 455  35547.57             2   47139.43
## 456  41099.00             2   35547.57
## 457  35859.57             2   41099.00
## 458  44524.57             2   35859.57
## 459  48554.29             2   44524.57
## 460  51554.29             2   48554.29
## 461  47810.29             2   51554.29
## 462  50490.00             2   47810.29
## 463  50720.71             2   50490.00
## 464  52720.71             2   50720.71
## 465  52145.57             2   52720.71
## 466  55515.57             2   52145.57
## 467  52457.00             2   55515.57
## 468  58239.57             2   52457.00
## 469  50523.57             2   58239.57
## 470  47788.57             2   50523.57
## 471  46170.00             2   47788.57
## 472  42305.57             2   46170.00
## 473  46605.57             2   42305.57
## 474  55149.57             2   46605.57
## 475  48769.57             2   55149.57
## 476  50719.43             2   48769.57
## 477  44753.71             2   50719.43
## 478  42898.00             2   44753.71
## 479  46141.14             2   42898.00
## 480  34022.57             2   46141.14
## 481  26651.86             2   34022.57
## 482  28791.86             2   26651.86
## 483  31879.00             2   28791.86
## 484  33584.71             2   31879.00
## 485  34690.43             2   33584.71
## 486  27410.43             2   34690.43
## 487  41755.00             2   27410.43
## 488  49379.57             2   41755.00
## 489  57198.86             2   49379.57
## 490  51144.57             2   57198.86
## 491  56677.43             2   51144.57
## 492  65416.43             2   56677.43
## 493  69779.71             2   65416.43
## 494  54046.00             2   69779.71
## 495  43259.57             2   54046.00
## 496  40998.57             2   43259.57
## 497  41368.57             2   40998.57
## 498  42274.29             2   41368.57
## 499  35962.71             2   42274.29
## 500  38709.00             2   35962.71
## 501  44778.14             2   38709.00
## 502  51282.43             2   44778.14
## 503  52094.86             2   51282.43
## 504  52221.43             2   52094.86
## 505  45011.43             2   52221.43
## 506  46545.43             2   45011.43
## 507  42263.00             2   46545.43
## 508  45417.43             2   42263.00
## 509  45034.71             2   45417.43
## 510  37840.57             2   45034.71
## 511  39135.43             2   37840.57
## 512  38191.14             2   39135.43
## 513  39456.86             2   38191.14
## 514  42479.14             2   39456.86
## 515  34282.57             2   42479.14
## 516  28878.43             2   34282.57
## 517  56227.14             2   28878.43
## 518  65569.43             2   56227.14
## 519  69751.29             2   65569.43
## 520  62171.71             2   69751.29
## 521  63705.14             2   62171.71
## 522  79257.86             2   63705.14
## 523  87244.71             2   79257.86
## 524  58568.00             2   87244.71
## 525  52695.29             2   58568.00
## 526  48911.00             2   52695.29
## 527  53924.00             2   48911.00
## 528  53358.86             2   53924.00
## 529  42121.14             2   53358.86
## 530  47835.71             2   42121.14
## 531  62329.29             2   47835.71
## 532  56056.86             2   62329.29
## 533  59946.43             2   56056.86
## 534  64511.57             2   59946.43
## 535  61137.43             2   64511.57
## 536  55448.71             2   61137.43
## 537  47964.43             2   55448.71
## 538  46425.71             2   47964.43
## 539  55512.00             2   46425.71
## 540  55226.29             2   55512.00
## 541  46709.14             2   55226.29
## 542  49254.71             2   46709.14
## 543  49056.29             2   49254.71
## 544  49850.57             2   49056.29
## 545  39145.71             2   49850.57
## 546  29799.43             2   39145.71
## 547  34769.86             2   29799.43
## 548  44061.57             2   34769.86
## 549  43829.14             2   44061.57
## 550  45782.00             2   43829.14
## 551  38924.57             2   45782.00
## 552  49242.43             2   38924.57
## 553  50565.00             2   49242.43
## 554  38864.43             2   50565.00
## 555  49786.71             2   38864.43
## 556  58787.86             2   49786.71
## 557  58060.86             2   58787.86
## 558  62179.43             2   58060.86
## 559  57333.86             2   62179.43
## 560  70797.00             2   57333.86
## 561  89901.71             2   70797.00
## 562  78558.14             2   89901.71
## 563  65466.00             2   78558.14
## 564  70525.00             2   65466.00
## 565  68377.86             2   70525.00
## 566  69736.29             2   68377.86
## 567  60085.86             2   69736.29
## 568  41757.00             2   60085.86
## 569  49780.29             2   41757.00
## 570  56540.29             2   49780.29
## 571  57894.29             2   56540.29
## 572  60270.29             2   57894.29
## 573  61011.00             2   60270.29
## 574  57721.43             2   61011.00
## 575  71741.00             2   57721.43
## 576  59576.00             2   71741.00
## 577  52390.29             2   59576.00
## 578  61092.29             2   52390.29
## 579  62814.00             2   61092.29
## 580  54908.29             2   62814.00
## 581  62082.00             2   54908.29
## 582  57017.71             2   62082.00
## 583  53634.43             2   57017.71
## 584  69169.00             2   53634.43
## 585  52488.14             2   69169.00
## 586  60895.57             2   52488.14
## 587  59856.57             2   60895.57
## 588  52670.00             2   59856.57
## 589  51874.57             2   52670.00
## 590  52190.57             2   51874.57
## 591  41562.43             2   52190.57
## 592  44764.14             2   41562.43
## 593  38612.71             2   44764.14
## 594  43473.14             2   38612.71
## 595  53505.00             2   43473.14
## 596  45870.86             2   53505.00
## 597  52578.00             2   45870.86
## 598  55300.00             2   52578.00
## 599  61789.71             2   55300.00
## 600  57391.71             2   61789.71
## 601  62902.29             2   57391.71
## 602  53250.43             2   62902.29
## 603  55402.57             2   53250.43
## 604  56291.29             2   55402.57
## 605  58933.57             2   56291.29
## 606  59590.71             2   58933.57
## 607  59065.00             2   59590.71
## 608  52399.57             2   59065.00
## 609  60483.43             2   52399.57
## 610  58262.71             2   60483.43
## 611  54939.71             2   58262.71
## 612  51169.00             2   54939.71
## 613  43113.29             2   51169.00
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   456 50777.54 14989.560
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00  53358.86  42121.14  47835.71  62329.29  56056.86  59946.43
## [533]  64511.57  61137.43  55448.71  47964.43  46425.71  55512.00  55226.29
## [540]  46709.14  49254.71  49056.29  49850.57  39145.71  29799.43  34769.86
## [547]  44061.57  43829.14  45782.00  38924.57  49242.43  50565.00  38864.43
## [554]  49786.71  58787.86  58060.86  62179.43  57333.86  70797.00  89901.71
## [561]  78558.14  65466.00  70525.00  68377.86  69736.29  60085.86  41757.00
## [568]  49780.29  56540.29  57894.29  60270.29  61011.00  57721.43  71741.00
## [575]  59576.00  52390.29  61092.29  62814.00  54908.29  62082.00  57017.71
## [582]  53634.43  69169.00  52488.14  60895.57  59856.57  52670.00  51874.57
## [589]  52190.57  41562.43  44764.14  38612.71  43473.14  53505.00  45870.86
## [596]  52578.00  55300.00  61789.71  57391.71  62902.29  53250.43  55402.57
## [603]  56291.29  58933.57  59590.71  59065.00  52399.57  60483.43  58262.71
## [610]  54939.71  51169.00  43113.29
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##             2             3             4             5             6 
##   1958.919826   4014.675794   -512.529853   2460.214210  -2919.123892 
##             7             8             9            10            11 
##    537.104251  -5628.856334  -1218.173041  -3999.663495   -483.425737 
##            12            13            14            15            16 
##  -4995.821982  -1704.806127   -994.517598    290.726640  -3309.197428 
##            17            18            19            20            21 
##   -464.391610  -2204.130497   6522.598098  -1525.779439  -1215.906496 
##            22            23            24            25            26 
##   1461.738054  -1177.788611    235.360364   1703.568396  -7071.277069 
##            27            28            29            30            31 
##    905.490584   8171.192534    491.314373     59.903609  -2330.541567 
##            32            33            34            35            36 
##   1617.817190   4631.029551   1231.690491   2500.473426  -1741.974470 
##            37            38            39            40            41 
##   4704.087854   4360.472602  -2180.179445  -2921.348401  -1088.443742 
##            42            43            44            45            46 
## -10733.663241   7183.881815   2542.028632   1380.833082   8132.195455 
##            47            48            49            50            51 
##    795.313880   6631.833108   6873.474312  -5672.360184  -4673.193338 
##            52            53            54            55            56 
##  -5002.740988  -7931.382110   6044.623939  -4086.271070  -4945.093366 
##            57            58            59            60            61 
##   3760.529392    845.468824    -59.382427    118.501512  -5015.228441 
##            62            63            64            65            66 
##  18058.372930   3771.555547  -3493.030795   6021.390542   7490.683352 
##            67            68            69            70            71 
##  14844.682057   2027.288583 -12902.376807  -1172.914928   4746.834244 
##            72            73            74            75            76 
##  -4760.707711  -4333.364201 -10480.820712   2372.106772  -5456.008357 
##            77            78            79            80            81 
##    958.733310  -6946.271765    405.768618  -2471.569961  -2820.120536 
##            82            83            84            85            86 
##  -4069.764975   -698.517225   2168.909878   3660.102139    427.076930 
##            87            88            89            90            91 
##   -522.669300    158.663084   4271.269145  -1144.385407   1155.415750 
##            92            93            94            95            96 
##  -2047.989888  -1051.016252    161.256094    262.837327  -7491.140998 
##            97            98            99           100           101 
##   2307.881910  -8650.251840  -3071.495266  -4183.835559  -1904.284831 
##           102           103           104           105           106 
##  -1425.114330   3025.406567  -2444.173112   2480.907469  -1229.638011 
##           107           108           109           110           111 
##    896.654265   2533.243357  -3173.946085  -4772.549952   -942.252269 
##           112           113           114           115           116 
##   1814.826301  11636.442294  -1169.961982   2719.525340   4335.781150 
##           117           118           119           120           121 
##   3611.460417   -967.760183  -4611.740338  -3681.352835   2318.835398 
##           122           123           124           125           126 
##  -1708.677173   1343.863026   8875.812361    955.484192    234.562080 
##           127           128           129           130           131 
##  -2428.364123   2710.491866   7129.230294   1153.650654  -8364.892339 
##           132           133           134           135           136 
##   1778.546188   4179.943309  -3081.435183  -1380.128156   -833.658707 
##           137           138           139           140           141 
##  -3870.629612   1151.287561   -510.340534  -2931.229865   1672.886727 
##           142           143           144           145           146 
##  -1902.189336  -7866.860827   1925.455427  -3557.543674   1998.389006 
##           147           148           149           150           151 
##   -325.821396    961.066156   -402.325777   1311.295392   1165.449129 
##           152           153           154           155           156 
##   3350.896311  -4831.277614  -1198.066237  -3268.202959   5895.132904 
##           157           158           159           160           161 
##   9755.446769  -3399.334421  -4765.499469   3585.641253    236.425503 
##           162           163           164           165           166 
##   2754.594298  -5811.701369  -6702.582211   4145.851557  17445.546592 
##           167           168           169           170           171 
##   3866.055452   -135.663943  -2201.997895   -897.199040   3778.572811 
##           172           173           174           175           176 
##     -9.579528  -7868.504306   2981.491784   4475.253416    817.772940 
##           177           178           179           180           181 
##   8942.576325  -8975.273927  -3307.791651 -10618.256142 -11219.425998 
##           182           183           184           185           186 
##   1158.402539   9256.836692  -1351.328628   6000.587781   6693.789891 
##           187           188           189           190           191 
##  13359.492762   8749.894448  -3689.110338   2763.974774  10666.372177 
##           192           193           194           195           196 
##  -1270.632514  -2122.727556 -10010.707706  -6214.055651   1316.496699 
##           197           198           199           200           201 
##  -5134.017962  -9744.755132   5349.527553  -3029.306388  -1691.840858 
##           202           203           204           205           206 
##   -786.465656   6518.065194   9979.131014    769.759507   3110.255502 
##           207           208           209           210           211 
##   3301.028457   6004.435942  13094.119809  -5324.813661 -11020.408181 
##           212           213           214           215           216 
##  -5518.874094 -10497.383282  -5081.482183   1489.539268 -13011.888709 
##           217           218           219           220           221 
##  16281.334288   7886.407610   1683.577677  26852.356223  12932.614441 
##           222           223           224           225           226 
##   7818.318832  14528.495956  -3332.873794  -1259.649063   4193.231160 
##           227           228           229           230           231 
##    771.556588   3122.586891   9373.866885   6253.914294  -1464.274750 
##           232           233           234           235           236 
##  -1444.610201   9758.372390 -11113.784121  -7033.596162  -8377.432254 
##           237           238           239           240           241 
## -10024.554604   3064.432613   1385.792188  -8238.932566  -9001.292934 
##           242           243           244           245           246 
##   9015.858943  -7732.173186   2458.998061 -10286.429284  -4122.130217 
##           247           248           249           250           251 
##   1341.171397    961.170434 -12327.437948   3533.366165   2016.265026 
##           252           253           254           255           256 
##   4205.835170   2183.478498  -1083.928725  11209.278198  21058.134891 
##           257           258           259           260           261 
##   3553.681626  -3920.959910   4386.493620  -1404.165610   3986.310067 
##           262           263           264           265           266 
##  -4588.645901 -10696.846520  -4644.373256   -478.437839  -5142.113425 
##           267           268           269           270           271 
##   8783.950401  -4183.590249   4246.635344  -2007.642410   4509.775908 
##           272           273           274           275           276 
##    828.798316   7424.526102  -1230.732343  12180.244801  -4334.118426 
##           277           278           279           280           281 
##   1910.531265   -186.903868   8018.735605  -4833.893348  -2571.063408 
##           282           283           284           285           286 
## -11134.786973  -2644.455225  18666.211958   7965.748296   2968.982200 
##           287           288           289           290           291 
##   -391.832900   1115.687723   6597.659109   7119.583091 -18498.683286 
##           292           293           294           295           296 
## -11047.682241  -8121.083311   9613.054052   3123.254728  -1094.984316 
##           297           298           299           300           301 
##  27478.858916  10371.065555   5259.401755   9879.543022   3258.824692 
##           302           303           304           305           306 
##   -649.790109   8230.473717 -23929.858172  -3401.883243    -73.245766 
##           307           308           309           310           311 
##  -6865.331679  -3922.457342   2960.362629  -9126.615406  -3228.655877 
##           312           313           314           315           316 
##  -8191.245356   1515.990588  -3161.502912   2033.594899  -4060.416577 
##           317           318           319           320           321 
##  27450.045340   -506.464706   3485.732186  11036.818038   5864.937572 
##           322           323           324           325           326 
##  32675.194369   5633.895436 -20430.709511   2080.773800   1388.460963 
##           327           328           329           330           331 
##  -6201.182735  -1541.148932 -33098.115642    823.966673  -2317.407425 
##           332           333           334           335           336 
##    -96.089860  -3143.516214   4109.669816   -358.743867  -6862.080502 
##           337           338           339           340           341 
##  -3064.405417  -2142.992197  -7626.751778   3866.945353  -1302.610965 
##           342           343           344           345           346 
##  -1662.419061   -915.280798    263.561122    584.184131  -1500.837566 
##           347           348           349           350           351 
##  -9331.966213 -13156.353039   2284.071510  -4302.108964  -3645.688013 
##           352           353           354           355           356 
##  -5969.093846   1742.806357   1416.205974   2815.417432  -3669.281905 
##           357           358           359           360           361 
##   -436.685389    765.723332   7117.916262    439.187299    125.876828 
##           362           363           364           365           366 
##   2746.276775  -2569.027410   -717.648812  -8588.099585  -4528.046445 
##           367           368           369           370           371 
##  -6133.931704  -4898.326737  -7215.766039   5023.341082    438.397154 
##           372           373           374           375           376 
##   7205.103482  -7489.506516  -2164.270982  -3292.311854  -2381.523350 
##           377           378           379           380           381 
## -12374.031290   1915.563761 -10583.094027   5693.389840   9398.707618 
##           382           383           384           385           386 
##   3267.639267  -2232.824473   1753.681215   6905.776872  11616.684993 
##           387           388           389           390           391 
##  -5529.671112  -5152.650937      4.108088   8720.102218   2025.647658 
##           392           393           394           395           396 
##  11431.939175  -9607.169191   2953.959563    901.755190    745.664783 
##           397           398           399           400           401 
##   -476.351772   -398.591826 -14332.870425   8582.858099  -1047.087586 
##           402           403           404           405           406 
##  -1242.578129   7107.154534  -7756.738524  -1174.626234  -2409.371456 
##           407           408           409           410           411 
##  -5704.102557  -2770.242861  -3830.993614  -8678.260431   6171.409017 
##           412           413           414           415           416 
##   1740.256166  -7251.021833  -7608.206285  14271.094867   3973.954316 
##           417           418           419           420           421 
##   4672.050899  -7832.638114  -4598.431604  -2480.236010   2934.095315 
##           422           423           424           425           426 
## -13868.212561  -2732.068633  -9036.853569   3036.704379   7045.184095 
##           427           428           429           430           431 
##   6704.463938  -3810.355416  -3969.146644  -4591.935122  -1680.884807 
##           432           433           434           435           436 
##  -5602.516744  -6542.013694  -5891.745350  -1353.671071   -794.288177 
##           437           438           439           440           441 
##  -4906.444251   2636.445949   4930.877482  -4920.335751  -2047.033622 
##           442           443           444           445           446 
##   1685.265182  -3706.195864   2950.294084  -6434.110078 -12002.428770 
##           447           448           449           450           451 
##  -4469.841400   9680.461876  -1908.984963   4875.202391  -5706.000229 
##           452           453           454           455           456 
##   -992.581823    516.967731   3171.870840 -12094.967763   3464.482712 
##           457           458           459           460           461 
##  -6567.863686   6620.683772   3169.326757   2690.206169  -3643.893531 
##           462           463           464           465           466 
##   2268.265180    185.403743   1986.212743   -315.663248   3550.895866 
##           467           468           469           470           471 
##  -2417.220893   6006.018849  -6702.459992  -2775.723563  -2032.987431 
##           472           473           474           475           476 
##  -4499.995546   3136.422883   7967.946646  -5788.657301   1669.478538 
##           477           478           479           480           481 
##  -5979.677215  -2684.793239   2160.511289 -12758.081253  -9666.026131 
##           482           483           484           485           486 
##  -1162.397844     77.140561   -882.481083  -1249.423484  -9484.060231 
##           487           488           489           490           491 
##  11145.819804   6385.767847   7622.253504  -5182.942072   5576.982942 
##           492           493           494           495           496 
##   9539.099066   6357.424354 -13143.404939 -10345.870625  -3294.228822 
##           497           498           499           500           501 
##   -972.157015   -385.888359  -7479.423221    716.062249   4414.153837 
##           502           503           504           505           506 
##   5678.544520    875.390265    300.538027  -7018.739512    740.133434 
##           507           508           509           510           511 
##  -4866.699451   1985.034774  -1121.107678  -7984.827816   -478.788254 
##           512           513           514           515           516 
##  -2541.010334   -460.031333   1469.478984  -9336.432886  -7663.930010 
##           517           518           519           520           521 
##  24350.540541  10080.860698   6196.900698  -4993.146375   3084.230758 
##           522           523           524           525           526 
##  17313.034083  11872.197690 -23700.102027  -4814.299954  -3528.280498 
##           527           528           529           530           531 
##   4751.945266   -141.254190 -10891.043027   4525.795202  14085.596716 
##           532           533           534           535           536 
##  -4700.096859   4604.879690   5811.896617  -1503.637955  -4279.230107 
##           537           538           539           540           541 
##  -6852.070099  -1929.102323   8485.657861    355.148274  -7915.318421 
##           542           543           544           545           546 
##   1983.669394   -412.520442    553.081867 -10837.535006 -10941.604962 
##           547           548           549           550           551 
##   2098.094218   7098.506653  -1156.077380    997.450821  -7546.009318 
##           552           553           554           555           556 
##   8692.322388   1106.800919 -11735.634463   9288.533434   8859.739973 
##           557           558           559           560           561 
##    361.454168   5107.693090  -3293.715212  14352.932013  21834.018873 
##           562           563           564           565           566 
##  -6003.924137  -9302.406676   7059.911754    544.997436   3757.197364 
##           567           568           569           570           571 
##  -7066.053019 -17063.052779   6784.755399   6617.718758   2135.360767 
##           572           573           574           575           576 
##   3342.362436   2031.717759  -1897.361620  14962.315799  -9306.713452 
##           577           578           579           580           581 
##  -5989.573453   8916.332019   3125.030441  -6267.154397   7732.089290 
##           582           583           584           585           586 
##  -3525.741498  -2536.692242  15918.894958 -14173.991785   8635.131148 
##           587           588           589           590           591 
##    337.438407  -5952.095159   -542.878229    459.868205 -10441.098487 
##           592           593           594           595           596 
##   1936.599008  -6979.082634   3192.283704   9027.809318  -7267.503598 
##           597           598           599           600           601 
##   6030.702967   2961.980066   7101.610557  -2899.391785   6408.265804 
##           602           603           604           605           606 
##  -8001.234473   2483.999148   1514.625244   3389.624756   1765.506468 
##           607           608           609           610           611 
##    672.437010  -5539.107424   8299.457901   -900.588372  -2306.297904 
##           612           613 
##  -3208.045088  -8008.250727 
## 
## $fitted.values
##        2        3        4        5        6        7        8        9 
## 17310.37 20124.32 24328.67 24049.93 26375.84 23739.61 24447.57 19735.32 
##       10       11       12       13       14       15       16       17 
## 19474.95 16848.71 17617.11 14384.66 14435.23 15092.13 16768.91 15108.53 
##       18       19       20       21       22       23       24       25 
## 16131.13 15511.97 22511.78 21606.48 21092.40 22960.36 22294.21 22939.15 
##       26       27       28       29       30       31       32       33 
## 24763.56 18762.80 20468.81 28214.69 28271.67 27948.40 25605.47 26991.54 
##       34       35       36       37       38       39       40       41 
## 30789.74 31134.10 32526.83 30066.48 34082.53 37253.18 34343.63 31191.73 
##       42       43       44       45       46       47       48       49 
## 30052.95 20742.40 28173.40 30581.45 31657.95 38416.26 37916.74 42524.53 
##       50       51       52       53       54       55       56       57 
## 46711.36 39494.48 34126.31 29207.10 22431.52 28648.13 25268.66 21609.47 
##       58       59       60       61       62       63       64       65 
## 25966.39 27211.24 27504.78 27911.80 23830.91 40228.59 42051.03 37352.47 
##       66       67       68       69       70       71       72       73 
## 41510.32 46368.60 56912.28 54949.23 40364.63 37899.59 40882.28 35248.94 
##       74       75       76       77       78       79       80       81 
## 30754.25 21566.18 24730.29 20703.55 22765.27 17720.37 19712.28 18947.83 
##       82       83       84       85       86       87       88       89 
## 17986.91 16078.37 17341.23 20907.18 25273.35 26251.67 26276.34 26885.87 
##       90       91       92       93       94       95       96       97 
## 30962.81 29807.01 30794.70 28881.73 28090.89 28454.73 28856.57 22508.98 
##       98       99      100      101      102      103      104      105 
## 25488.82 18600.64 17470.12 15533.71 15829.97 16499.45 20919.89 20014.09 
##      106      107      108      109      110      111      112      113 
## 23484.21 23276.63 24933.19 27776.37 25303.69 21788.68 22060.89 24676.27 
##      114      115      116      117      118      119      120      121 
## 35413.96 33627.90 35443.93 38407.25 40340.33 38055.74 32937.21 29321.31 
##      122      123      124      125      126      127      128      129 
## 31379.82 29679.85 30847.62 38358.66 38005.30 37077.79 33977.94 35738.34 
##      130      131      132      133      134      135      136      137 
## 41073.21 40520.04 31824.45 33074.49 36227.01 32679.56 31085.66 30181.34 
##      138      139      140      141      142      143      144      145 
## 26778.57 28176.48 27948.80 25662.11 27662.90 26303.72 19980.54 22975.69 
##      146      147      148      149      150      151      152      153 
## 20827.75 23770.11 24303.79 25875.61 26055.56 27690.41 28975.96 31972.71 
##      154      155      156      157      158      159      160      161 
## 27495.78 26767.35 24351.15 30176.41 41419.76 39769.50 37165.22 42126.86 
##      162      163      164      165      166      167      168      169 
## 43518.98 46894.99 42413.87 37775.86 43137.74 59249.52 61435.81 59868.43 
##      170      171      172      173      174      175      176      177 
## 56731.20 55149.14 57820.15 56855.65 49237.79 52028.32 55727.23 55763.00 
##      178      179      180      181      182      183      184      185 
## 62808.56 53421.79 50210.68 41126.71 32764.88 36232.16 46217.61 45679.98 
##      186      187      188      189      190      191      192      193 
## 51563.21 57241.08 67898.11 73119.25 66887.60 67078.77 74066.49 69793.44 
##      194      195      196      197      198      199      200      201 
## 65368.56 54738.06 48837.93 50245.59 45891.76 38152.04 44501.73 42749.84 
##      202      203      204      205      206      207      208      209 
## 42392.04 42864.79 49579.44 58364.81 57998.74 59703.40 61339.85 65086.74 
##      210      211      212      213      214      215      216      217 
## 74442.67 66617.98 54945.02 49616.81 40718.34 37711.60 40788.89 30925.67 
##      218      219      220      221      222      223      224      225 
## 47700.88 54936.14 55827.50 78326.96 85734.40 87714.22 95216.87 86273.51 
##      226      227      228      229      230      231      232      233 
## 80342.05 79928.87 76617.98 75789.28 80470.94 81819.27 76319.75 71588.63 
##      234      235      236      237      238      239      240      241 
## 77176.21 63980.02 56109.58 48154.27 39863.85 44006.78 46134.36 39661.58 
##      242      243      244      245      246      247      248      249 
## 33415.00 43577.32 37891.43 41781.14 34135.42 32856.40 36468.97 39259.87 
##      250      251      252      253      254      255      256      257 
## 30196.49 36065.16 39822.16 44956.24 47642.79 47141.29 57321.87 74614.60 
##      258      259      260      261      262      263      264      265 
## 74431.82 67820.65 69285.17 65550.12 66979.36 60809.99 50209.94 46283.72 
##      266      267      268      269      270      271      272      273 
## 46490.68 42642.91 51344.16 47660.79 51759.07 49897.65 53917.49 54210.05 
##      274      275      276      277      278      279      280      281 
## 60157.16 57819.04 67378.98 61374.75 61582.33 59950.69 65626.46 59430.21 
##      282      283      284      285      286      287      288      289 
## 56034.22 45708.60 44124.07 61154.97 66620.45 67025.12 64472.88 63570.91 
##      290      291      292      293      294      295      296      297 
## 67525.13 71389.68 52608.25 42825.94 36906.95 47107.75 50311.70 49436.00 
##      298      299      300      301      302      303      304      305 
## 73349.65 79225.60 79885.46 84444.03 82663.65 77751.95 81178.29 56370.31 
##      306      307      308      309      310      311      312      313 
## 52675.10 52358.62 46221.31 43463.35 47024.62 39663.80 38400.82 33025.87 
##      314      315      316      317      318      319      320      321 
## 36766.22 35957.12 39743.85 37751.81 63237.04 61103.41 62708.04 70612.78 
##      322      323      324      325      326      327      328      329 
## 72972.23 98156.39 96553.00 72665.37 71477.25 69853.75 61899.43 59055.26 
##      330      331      332      333      334      335      336      337 
## 29354.46 32998.98 33433.38 35726.23 35074.76 40774.46 41837.51 37140.55 
##      338      339      340      341      342      343      344      345 
## 36364.14 36489.32 31862.91 37791.90 38447.56 38703.00 39568.58 41333.67 
##      346      347      348      349      350      351      352      353 
## 43134.41 42888.97 35915.92 26593.79 31876.11 30750.40 30345.24 27989.48 
##      354      355      356      357      358      359      360      361 
## 32613.79 36324.30 40735.85 38945.97 40191.56 42305.08 49614.10 50158.27 
##      362      363      364      365      366      367      368      369 
## 50357.58 52792.03 50304.79 49755.81 42486.76 39716.22 35937.76 33742.34 
##      370      371      372      373      374      375      376      377 
## 29846.09 37049.03 39309.33 47102.94 41144.84 40598.45 39152.81 38691.03 
##      378      379      380      381      382      383      384      385 
## 29665.15 34209.67 27342.32 35465.86 45678.50 49202.40 47495.89 49464.37 
##      386      387      388      389      390      391      392      393 
## 55612.03 64986.96 58277.37 52810.03 52541.90 59835.50 60352.78 68920.45 
##      394      395      396      397      398      399      400      401 
## 58153.04 59701.67 59266.91 58756.78 57261.31 56037.30 42950.14 51435.80 
##      402      403      404      405      406      407      408      409 
## 50447.86 49426.13 55752.88 48382.20 47701.37 46047.53 41775.10 40619.42 
##      410      411      412      413      414      415      416      417 
## 38705.83 32868.73 40649.89 43542.16 38276.49 33421.91 48120.47 51920.52 
##      418      419      420      421      422      423      424      425 
## 55804.07 48360.86 44726.95 43418.33 46963.07 35516.93 35249.28 29574.87 
##      426      427      428      429      430      431      432      433 
## 35099.67 43330.39 50142.36 46945.43 44048.22 41009.17 40898.66 37417.44 
##      434      435      436      437      438      439      440      441 
## 33600.75 30866.96 32424.72 34252.59 32280.41 37089.98 43223.34 40013.46 
##      442      443      444      445      446      447      448      449 
## 39722.88 42694.34 40604.99 44548.11 39850.29 30986.84 29837.82 41062.70 
##      450      451      452      453      454      455      456      457 
## 40747.94 46333.43 42020.30 42365.89 43967.56 47642.54 37634.52 42427.44 
##      458      459      460      461      462      463      464      465 
## 37903.89 45384.96 48864.08 51454.18 48221.73 50535.31 50734.50 52461.23 
##      466      467      468      469      470      471      472      473 
## 51964.68 54874.22 52233.55 57226.03 50564.29 48202.99 46805.57 43469.15 
##      474      475      476      477      478      479      480      481 
## 47181.62 54558.23 49049.95 50733.39 45582.79 43980.63 46780.65 36317.88 
##      482      483      484      485      486      487      488      489 
## 29954.25 31801.86 34467.20 35939.85 36894.49 30609.18 42993.80 49576.60 
##      490      491      492      493      494      495      496      497 
## 56327.51 51100.45 55877.33 63422.29 67189.40 53605.44 44292.80 42340.73 
##      498      499      500      501      502      503      504      505 
## 42660.17 43442.14 37992.94 40363.99 45603.88 51219.47 51920.89 52030.17 
##      506      507      508      509      510      511      512      513 
## 45805.30 47129.70 43432.39 46155.82 45825.40 39614.22 40732.15 39916.89 
##      514      515      516      517      518      519      520      521 
## 41009.66 43619.00 36542.36 31876.60 55488.57 63554.39 67164.86 60620.91 
##      522      523      524      525      526      527      528      529 
## 61944.82 75372.52 82268.10 57509.59 52439.28 49172.05 53500.11 53012.19 
##      530      531      532      533      534      535      536      537 
## 43309.92 48243.69 60756.95 55341.55 58699.67 62641.07 59727.94 54816.50 
##      538      539      540      541      542      543      544      545 
## 48354.82 47026.34 54871.14 54624.46 47271.04 49468.81 49297.49 49983.25 
##      546      547      548      549      550      551      552      553 
## 40741.03 32671.76 36963.06 44985.22 44784.55 46470.58 40550.11 49458.20 
##      554      555      556      557      558      559      560      561 
## 50600.06 40498.18 49928.12 57699.40 57071.74 60627.57 56444.07 68067.70 
##      562      563      564      565      566      567      568      569 
## 84562.07 74768.41 63465.09 67832.86 65979.09 67151.91 58820.05 42995.53 
##      570      571      572      573      574      575      576      577 
## 49922.57 55758.92 56927.92 58979.28 59618.79 56778.68 68882.71 58379.86 
##      578      579      580      581      582      583      584      585 
## 52175.95 59688.97 61175.44 54349.91 60543.46 56171.12 53250.11 66662.13 
##      586      587      588      589      590      591      592      593 
## 52260.44 59519.13 58622.10 52417.45 51730.70 52003.53 42827.54 45591.80 
##      594      595      596      597      598      599      600      601 
## 40280.86 44477.19 53138.36 46547.30 52338.02 54688.10 60291.11 56494.02 
##      602      603      604      605      606      607      608      609 
## 61251.66 52918.57 54776.66 55543.95 57825.21 58392.56 57938.68 52183.97 
##      610      611      612      613 
## 59163.30 57246.01 54377.05 51121.54 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8346
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original     bias    std. error
## t1*    7.151488  0.5605368    3.513515
## t2* 1770.826614 23.0984103  219.996315
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value   Upper CI
## 1 interrupt_var    2.764631       7.296377   14.20165
## 2    lag_depvar 1452.184561    1781.009633 2176.20922

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
   dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"electrodomésticos/mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
                                            gasto=="Chromecast"~"electrodomésticos/mantención casa",
                                            gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"electrodomésticos/mantención casa",
                                            gasto=="Sopapo"~"electrodomésticos/mantención casa",
                                            gasto=="filtro agua"~"electrodomésticos/mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Transporte",
                                            gasto=="Uber Reñaca"~"Transporte",
                                            gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
                                            gasto=="Aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
                                            gasto=="Pila estufa"~"electrodomésticos/mantención casa",
                                            gasto=="Reloj"~"electrodomésticos/mantención casa",
                                            gasto=="Arreglo"~"electrodomésticos/mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +

  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03"))))

 # scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start = 
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")
library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Mon Aug 21 00:56:09 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Aug 21 00:56:17 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Aug 21 00:56:26 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Aug 21 00:56:35 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Aug 21 00:56:43 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Aug 21 00:56:52 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Aug 21 00:57:01 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Aug 21 00:57:09 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Aug 21 00:57:18 2023
##  =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Aug 21 00:57:27 2023
##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
  dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Otros",
                                            gasto=="Uber Reñaca"~"Otros",
                                            gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021|2022",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("202",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_23 %>% 
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))
Item 2023 2022 2021 2020
Agua 3.630286 5.410333 5.629750 6.5064884
Comida 351.597429 310.278417 314.087500 342.1973488
Comunicaciones 0.000000 0.000000 0.000000 0.0000000
Electricidad 39.311429 47.072333 38.297667 33.5250930
Enceres 26.902429 20.086417 17.443792 25.0026047
Farmacia 2.854286 1.831667 7.913875 8.7989302
Gas/Bencina 43.473714 44.325000 28.954333 28.0539535
Diosi 11.984286 31.180667 41.934250 35.7155349
donaciones/regalos 0.000000 0.000000 7.170083 6.3888140
Electrodomésticos/ Mantención casa 0.000000 3.944000 30.269500 19.2899535
VTR 12.567143 25.156667 22.121792 19.7269302
Netflix 5.771429 7.151583 7.090167 7.1983953
Otros 0.000000 3.151083 1.575542 0.8793721
Total 498.092429 499.588167 522.488250 533.2834186
## Joining with `by = join_by(word)`


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")

tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
##   = T)`.
## Caused by warning:
## !  41 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:2078, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)


fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)

La proyección de la UF a 298 días más 2023-09-09 00:04:58 sería de: 36.613 pesos// Percentil 95% más alto proyectado: 39.771,07

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 36179.82 36177.46
Lo.80 36191.63 36187.51
Point.Forecast 36612.72 37560.89
Hi.80 38384.88 42320.21
Hi.95 39357.46 44839.64


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(1,0,0) with non-zero mean 
## 
## Coefficients:
##          ar1       mean
##       0.2450  1007.9316
## s.e.  0.1372    29.9385
## 
## sigma^2 = 28818:  log likelihood = -352.89
## AIC=711.78   AICc=712.26   BIC=717.75
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(1,0,0) errors 
## 
## Coefficients:
##          ar1  intercept     xreg
##       0.2217   714.6894   9.5292
## s.e.  0.1389   312.3797  10.0966
## 
## sigma^2 = 28928:  log likelihood = -352.46
## AIC=712.93   AICc=713.74   BIC=720.88
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 747.9152 664.7484 710.2107
Lo.80 866.2449 783.5362 794.5381
Point.Forecast 1089.7748 1007.9316 982.1172
Hi.80 1313.3048 1232.3271 1272.7341
Hi.95 1431.6344 1351.1148 1459.9263


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 55 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Tami Andrés
1 marzo_2019 175533 68268
2 abril_2019 152640 55031
3 mayo_2019 152985 192219
4 junio_2019 291067 84961
5 julio_2019 241389 205893


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  sjPlot::theme_sjplot2() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] CausalImpact_1.3.0  bsts_0.9.9          BoomSpikeSlab_1.2.5
##  [4] Boom_0.9.11         scales_1.2.1        ggiraph_0.8.7      
##  [7] tidytext_0.4.1      DT_0.28             autoplotly_0.1.4   
## [10] rvest_1.0.3         plotly_4.10.2       xts_0.13.1         
## [13] forecast_8.21       wordcloud_2.6       RColorBrewer_1.1-3 
## [16] SnowballC_0.7.1     tm_0.7-11           NLP_0.2-1          
## [19] tsibble_1.1.3       lubridate_1.9.2     forcats_1.0.0      
## [22] dplyr_1.1.2         purrr_1.0.1         tidyr_1.3.0        
## [25] tibble_3.2.1        ggplot2_3.4.3       tidyverse_2.0.0    
## [28] sjPlot_2.8.15       lattice_0.20-45     gridExtra_2.3      
## [31] plotrix_3.8-2       sparklyr_1.8.2      httr_1.4.7         
## [34] readxl_1.4.3        zoo_1.8-12          stringr_1.5.0      
## [37] stringi_1.7.12      data.table_1.14.8   reshape2_1.4.4     
## [40] fUnitRoots_4021.80  plyr_1.8.8          readr_2.1.4        
## 
## loaded via a namespace (and not attached):
##   [1] uuid_1.1-0          backports_1.4.1     systemfonts_1.0.4  
##   [4] selectr_0.4-2       lazyeval_0.2.2      splines_4.1.2      
##   [7] crosstalk_1.2.0     digest_0.6.31       htmltools_0.5.5    
##  [10] fansi_1.0.4         ggfortify_0.4.16    magrittr_2.0.3     
##  [13] tzdb_0.4.0          modelr_0.1.11       vroom_1.6.3        
##  [16] timechange_0.2.0    anytime_0.3.9       tseries_0.10-54    
##  [19] colorspace_2.1-0    xfun_0.39           crayon_1.5.2       
##  [22] jsonlite_1.8.4      lme4_1.1-34         glue_1.6.2         
##  [25] gtable_0.3.3        emmeans_1.8.8       sjstats_0.18.2     
##  [28] sjmisc_2.8.9        car_3.1-2           quantmod_0.4.24    
##  [31] abind_1.4-5         mvtnorm_1.2-2       DBI_1.1.3          
##  [34] ggeffects_1.2.3     Rcpp_1.0.10         viridisLite_0.4.2  
##  [37] xtable_1.8-4        performance_0.10.4  bit_4.0.5          
##  [40] htmlwidgets_1.6.2   timeSeries_4030.106 gplots_3.1.3       
##  [43] ellipsis_0.3.2      spatial_7.3-14      pkgconfig_2.0.3    
##  [46] farver_2.1.1        nnet_7.3-16         sass_0.4.5         
##  [49] dbplyr_2.3.3        janitor_2.2.0       utf8_1.2.3         
##  [52] tidyselect_1.2.0    labeling_0.4.2      rlang_1.1.0        
##  [55] munsell_0.5.0       cellranger_1.1.0    tools_4.1.2        
##  [58] cachem_1.0.7        cli_3.6.1           generics_0.1.3     
##  [61] sjlabelled_1.2.0    broom_1.0.5         evaluate_0.20      
##  [64] fastmap_1.1.1       yaml_2.3.7          knitr_1.43         
##  [67] bit64_4.0.5         caTools_1.18.2      nlme_3.1-153       
##  [70] slam_0.1-50         xml2_1.3.3          tokenizers_0.3.0   
##  [73] compiler_4.1.2      rstudioapi_0.14     curl_5.0.2         
##  [76] bslib_0.4.2         highr_0.10          fBasics_4022.94    
##  [79] Matrix_1.6-1        its.analysis_1.6.0  nloptr_2.0.3       
##  [82] urca_1.3-3          vctrs_0.6.1         pillar_1.9.0       
##  [85] lifecycle_1.0.3     lmtest_0.9-40       jquerylib_0.1.4    
##  [88] estimability_1.4.1  bitops_1.0-7        insight_0.19.3     
##  [91] R6_2.5.1            KernSmooth_2.23-20  janeaustenr_1.0.0  
##  [94] codetools_0.2-18    assertthat_0.2.1    boot_1.3-28        
##  [97] MASS_7.3-54         gtools_3.9.4        withr_2.5.0        
## [100] fracdiff_1.5-2      bayestestR_0.13.1   parallel_4.1.2     
## [103] hms_1.1.3           quadprog_1.5-8      timeDate_4022.108  
## [106] minqa_1.2.5         snakecase_0.11.0    rmarkdown_2.24     
## [109] carData_3.0-5       TTR_0.24.3          base64enc_0.1-3
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))